Securiti has been widely recognized as an industry innovator, being named “Most Innovative Start-up” at RSA Conference 2020, Leader in the IDC Marketplace, Leader in the Forrester Wave Privacy Management Software, and winner of the 2020 HPE-IAPP Innovation Award. The company is being built by proven serial entrepreneurs and executives who have successfully built and scaled high-growth global companies before. We have multiple backend development roles open and if you aspire to work on cutting-edge technologies and frameworks, side by side with some of the smartest technologists, this may be a unique place for you. You will be working as a part of a distributed agile team, building a new SaaS platform that underpins a suite of enterprise applications solving a variety of hard data analytics and enterprise problems.
Responsibilities:
Work with a team of data scientists on end-to-end productization of generative AI related products and/or applications.
Devise approaches and pipelines including, but not limited to, data gathering, machine learning, evaluation frameworks.
Work closely with software teams to integrate ML/DL modules ensuring best practices in design and implementation.
Minimum Qualifications:
Bachelor's degree in Computer Science or related field along with at least 2 years of experience working on development of core ML/DL applications.
Highly proficient using core classical machine-learning techniques in data clustering, regression, and classification.
Successfully applied more advanced classical machine-learning techniques to a real-world problem, such as support vector machines, gradient boosted decision trees, or random forests.
Proficient using core neural network architectures, including feed-forward, convolutional, and recurrent architectures.
Experience applying intermediate deep-learning training techniques such as distillation, quantization, contrastive learning, or variational auto-encoding.
Can describe the operation of advanced neural network designs such as generative adversarial models, or transformers.
Experience in natural language processing in an academic or industry setting.
Proficient using Keras (preferred), TensorFlow, or PyTorch.
Preferred Qualifications:
Master’s degree in Data Science or related field, with applied industry experience.
Experience with LangChain and related GenAI frameworks.
Experience using embedding based models in real world applications for a variety of NLP related tasks.
Experience applying zero-shot, few-shot learning and fine-tuning LLM models for domain specific tasks.
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